DeepPFP: a multi-task-aware architecture for protein function prediction.

ABSTRACT: Deriving protein function from protein sequences poses a significant challenge due to the intricate relationship between sequence and function. Deep learning has made remarkable strides in predicting sequence-function relationships. However, models tailored for specific tasks or protein types encounter difficulties when using transfer learning across domains. This is attributed to the fact that protein function relies heavily on structural characteristics rather than mere sequence information. Consequently, there is a pressing need for a model capable of capturing shared features among diverse sequence-function mapping tasks to address the generalization issue. In this study, we explore the potential of Model-Agnostic Meta-Learning combined with a protein language model called Evolutionary

SUBMITTER: Wang H 

PROVIDER: S-EPMC11794456 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature